Predictors of Surrogate Decision Makers Selecting Life-Sustaining Therapy for Severe Acute Brain Injury Patients: An Analysis of US Population Survey Data.

Background: Patients with a severe acute brain injury admitted to the intensive care unit often have a poor neurological prognosis. In these situations, a clinician is responsible for conducting a goals-of-care conversation with the patient's surrogate decision makers. The diversity in thought and background of surrogate decision makers can present challenges during these conversations.

Concerns of surrogate decision makers for patients with acute brain injury: A US population survey.

Objective: To determine whether groups of surrogates for patients with severe acute brain injury (SABI) with poor prognosis can be identified based on their prioritization of goals-of-care (GOC) decisional concerns, an online survey of 1,588 adults recruited via a probability-based panel representative of the US population was conducted.

Methods: Participants acted as a surrogate for a GOC decision for a hypothetical patient with SABI and were randomized to 1 of 2 prognostic scenarios: the patient likely being left with a range of severe functional disability (SD) or remaining in a vegetative state (VS). Participants prioritized a list of 12 decisional concerns via best-worst scaling.

Sex differences and attitudes toward living donor kidney transplantation among urban black patients on hemodialysis.

Background And Objectives: Living donor kidney transplantation, the treatment of choice for ESRD, is underused by women and blacks. To better understand sex differences in the context of potential barriers to living donor kidney transplantation, the Dialysis Patient Transplant Questionnaire was administered in two urban, predominantly black hemodialysis units.

Design, Setting, Participants, & Measurements: The Dialysis Patient Transplant Questionnaire was designed to study barriers to kidney transplantation from previously validated questions.

Model-implied instrumental variable-generalized method of moments (MIIV-GMM) estimators for latent variable models.

The common maximum likelihood (ML) estimator for structural equation models (SEMs) has optimal asymptotic properties under ideal conditions (e.g., correct structure, no excess kurtosis, etc.

Constrained versus unconstrained estimation in structural equation modeling.

Recently, R. D. Stoel, F.